How does the multi-head attention mechanism in Transformers improve upon single-head attention?
The multi-head attention mechanism in Transformers improves upon single-head attention by allowing the model to attend to different parts of the input sequence in multiple ways simultaneously. Single-head attention calculates attention weights based on a single set of query, key, and value matrices, which limits the model's ability to capture diverse relationships between words in the input sequence. Multi-head attention addresses this limitation by using multiple sets of query, key, and value matrices, each representing a different 'attention head'. Each attention head learns to attend to different aspects of the input sequence, capturing different types of dependencies between words. The outputs of the multiple attention heads are then concatenated and linearly transformed to produce the final output. This allows the model to capture more complex and nuanced relationships between words, leading to improved performance. For example, in machine translation, one attention head might focus on syntactic relationships between words, while another attention head might focus on semantic relationships. By combining the outputs of these multiple heads, the model can better understand the meaning of the input sequence and generate more accurate translations. Therefore, multi-head attention enhances the model's ability to capture diverse dependencies in the input sequence compared to single-head attention, improving the model's overall performance.